wip

bjajoh · Oct 17, 2020 · 39c77b4 · 39c77b4
1 parent 3780302
commit 39c77b4
Show file tree

Hide file tree

Showing 34 changed files with 3,782 additions and 3,782 deletions.
diff --git a/.gitignore b/.gitignore
@@ -1,46 +1,46 @@
-# Prerequisites
-*.d
-
-# Compiled Object files
-*.slo
-*.lo
-*.o
-*.obj
-
-# Precompiled Headers
-*.gch
-*.pch
-
-# Compiled Dynamic libraries
-*.so
-*.dylib
-*.dll
-
-# Fortran module files
-*.mod
-*.smod
-
-# Compiled Static libraries
-*.lai
-*.la
-*.a
-*.lib
-
-# Executables
-*.exe
-*.out
-*.app
-
-
-# gitignore template for Jupyter Notebooks
-# website: http://jupyter.org/
-
-.ipynb_checkpoints
-*/.ipynb_checkpoints/*
-
-# IPython
-profile_default/
-ipython_config.py
-
-# Remove previous ipynb_checkpoints
+# Prerequisites
+*.d
+
+# Compiled Object files
+*.slo
+*.lo
+*.o
+*.obj
+
+# Precompiled Headers
+*.gch
+*.pch
+
+# Compiled Dynamic libraries
+*.so
+*.dylib
+*.dll
+
+# Fortran module files
+*.mod
+*.smod
+
+# Compiled Static libraries
+*.lai
+*.la
+*.a
+*.lib
+
+# Executables
+*.exe
+*.out
+*.app
+
+
+# gitignore template for Jupyter Notebooks
+# website: http://jupyter.org/
+
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Remove previous ipynb_checkpoints
 #   git rm -r .ipynb_checkpoints/
diff --git a/README.md b/README.md
@@ -1,16 +1,16 @@
-# runway-light-inspection
-## Mobile robot for automated visual inspection of the approach lighting system at Aarhus Airport (AAR). 
-https://www.ilocator.com/proposal//cover.php?ProposalID=6ymasXDnLmC3NSEPzF7ANSlGMOQGCzl4Lx3Svz0prog&debug=yes
-
-### Running the camera
-``` 
-roslaunch ueye mono.launch
-``` 
-``` 
-roslaunch ueye stereo.launch
-``` 
-
-![Bilby Stampede](https://upload.wikimedia.org/wikipedia/en/b/b9/AAU_logo_2012.png)
-
-![Bilby Stampede](https://s3-eu-west-1.amazonaws.com/businessautomation/Proposal_Full_Images/iLocator-GmbH_AAR_5774.jpg)
-![Bilby Stampede](https://s3-eu-west-1.amazonaws.com/businessautomation/Proposal_Full_Images/iLocator-GmbH_inserts_5611.png)
+# runway-light-inspection
+## Mobile robot for automated visual inspection of the approach lighting system at Aarhus Airport (AAR). 
+https://www.ilocator.com/proposal//cover.php?ProposalID=6ymasXDnLmC3NSEPzF7ANSlGMOQGCzl4Lx3Svz0prog&debug=yes
+
+### Running the camera
+``` 
+roslaunch ueye mono.launch
+``` 
+``` 
+roslaunch ueye stereo.launch
+``` 
+
+![Bilby Stampede](https://upload.wikimedia.org/wikipedia/en/b/b9/AAU_logo_2012.png)
+
+![Bilby Stampede](https://s3-eu-west-1.amazonaws.com/businessautomation/Proposal_Full_Images/iLocator-GmbH_AAR_5774.jpg)
+![Bilby Stampede](https://s3-eu-west-1.amazonaws.com/businessautomation/Proposal_Full_Images/iLocator-GmbH_inserts_5611.png)
diff --git a/camera_processing/CMakeLists.txt b/camera_processing/CMakeLists.txt
@@ -1,3 +1,3 @@
-catkin_install_python(PROGRAMS scripts/light_detector_node.py
-  DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
+catkin_install_python(PROGRAMS scripts/light_detector_node.py
+  DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
 )
diff --git a/camera_processing/light_detector_node.py b/camera_processing/light_detector_node.py
@@ -1,80 +1,80 @@
-#!/usr/bin/env python
-# license removed for brevity
-import rospy
-
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.cluster import DBSCAN
-
-###Gaussian blut on image
-##blur = cv2.blur(img,(8,8))
-##
-##
-#### hsv filter
-##hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)
-##lights = cv2.inRange(hsv, (15,0,200), (180,255,255))
-##
-##
-#### Cluster light hypothesis by using DBSCAN
-##light_pixels = np.array(list(indices[:])).T
-##db = DBSCAN(eps=5, min_samples=40).fit(light_pixels)
-##
-##core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
-##core_samples_mask[db.core_sample_indices_] = True
-##labels = db.labels_
-##
-### Number of clusters in labels, ignoring noise if present.
-##n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
-##n_noise_ = list(labels).count(-1)
-##
-##print('Estimated number of clusters: %d' % n_clusters_)
-##print('Estimated number of noise points: %d' % n_noise_)
-##
-###plt.figure(figsize=(20,4))
-###data = np.array(list(indices))
-###xs = np.array(list(indices[1]))
-###ys = np.array(list(indices[0]))
-###plt.scatter(xs, ys, c=labels, cmap='jet')
-###
-###ax=plt.gca()                            # get the axis
-###ax.set_ylim(ax.get_ylim()[::-1])        # invert the axis
-###ax.xaxis.tick_top()                     # and move the X-Axis      
-###
-###plt.show()
-##
-##
-#### Calculate single location per cluster buttom middle of each cluster as homography input
-##light_locations =  np.zeros([0, 2])
-##
-##for cluster_id in range(np.max(labels)+1):
-##    light_locations = np.vstack((light_locations, np.array([np.mean(xs[labels == [cluster_id]]), np.max(ys[labels == [cluster_id]])])))
-##
-###plt.figure(figsize=(20,4))
-###plt.plot(light_locations[:,0], light_locations[:,1], 'rd')
-###
-###ax=plt.gca()                            # get the axis
-###ax.set_ylim(ax.get_ylim()[::-1])        # invert the axis
-###ax.xaxis.tick_top()                     # and move the X-Axis      
-###
-###plt.show()
-##
-##np.set_printoptions(threshold=100)
-##print(light_locations)
-
-
-def talker():
-    pub = rospy.Publisher('chatter', String, queue_size=10)
-    rospy.init_node('talker', anonymous=True)
-    rate = rospy.Rate(10) # 10hz
-    while not rospy.is_shutdown():
-        hello_str = "hello world %s" % rospy.get_time()
-        rospy.loginfo(hello_str)
-        pub.publish(hello_str)
-        rate.sleep()
-
-if __name__ == '__main__':
-    try:
-        talker()
-    except rospy.ROSInterruptException:
+#!/usr/bin/env python
+# license removed for brevity
+import rospy
+
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import DBSCAN
+
+###Gaussian blut on image
+##blur = cv2.blur(img,(8,8))
+##
+##
+#### hsv filter
+##hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)
+##lights = cv2.inRange(hsv, (15,0,200), (180,255,255))
+##
+##
+#### Cluster light hypothesis by using DBSCAN
+##light_pixels = np.array(list(indices[:])).T
+##db = DBSCAN(eps=5, min_samples=40).fit(light_pixels)
+##
+##core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
+##core_samples_mask[db.core_sample_indices_] = True
+##labels = db.labels_
+##
+### Number of clusters in labels, ignoring noise if present.
+##n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
+##n_noise_ = list(labels).count(-1)
+##
+##print('Estimated number of clusters: %d' % n_clusters_)
+##print('Estimated number of noise points: %d' % n_noise_)
+##
+###plt.figure(figsize=(20,4))
+###data = np.array(list(indices))
+###xs = np.array(list(indices[1]))
+###ys = np.array(list(indices[0]))
+###plt.scatter(xs, ys, c=labels, cmap='jet')
+###
+###ax=plt.gca()                            # get the axis
+###ax.set_ylim(ax.get_ylim()[::-1])        # invert the axis
+###ax.xaxis.tick_top()                     # and move the X-Axis      
+###
+###plt.show()
+##
+##
+#### Calculate single location per cluster buttom middle of each cluster as homography input
+##light_locations =  np.zeros([0, 2])
+##
+##for cluster_id in range(np.max(labels)+1):
+##    light_locations = np.vstack((light_locations, np.array([np.mean(xs[labels == [cluster_id]]), np.max(ys[labels == [cluster_id]])])))
+##
+###plt.figure(figsize=(20,4))
+###plt.plot(light_locations[:,0], light_locations[:,1], 'rd')
+###
+###ax=plt.gca()                            # get the axis
+###ax.set_ylim(ax.get_ylim()[::-1])        # invert the axis
+###ax.xaxis.tick_top()                     # and move the X-Axis      
+###
+###plt.show()
+##
+##np.set_printoptions(threshold=100)
+##print(light_locations)
+
+
+def talker():
+    pub = rospy.Publisher('chatter', String, queue_size=10)
+    rospy.init_node('talker', anonymous=True)
+    rate = rospy.Rate(10) # 10hz
+    while not rospy.is_shutdown():
+        hello_str = "hello world %s" % rospy.get_time()
+        rospy.loginfo(hello_str)
+        pub.publish(hello_str)
+        rate.sleep()
+
+if __name__ == '__main__':
+    try:
+        talker()
+    except rospy.ROSInterruptException:
         pass